class_docstr: We'll use this mock dataset reader for most of the tests.
  
  It utilizes a transformers tokenizer, since historically we've seen deadlocking
  issues when using these within subprocesses. So these tests also serve as
  regression tests against those issues.
  
  And unlike `MockOldDatasetReader` below, it implements all of the new API,
  specifically the `apply_token_indexers` method, so that it can be used
  with num_workers > 0.
code: def __init__(
      self, model: str = "epwalsh/bert-xsmall-dummy", **kwargs
  ) -> None:
      super().__init__(
          manual_distributed_sharding=True,
          manual_multiprocess_sharding=True,
          **kwargs,
      )
      self.tokenizer = PretrainedTransformerTokenizer(model)
      self.token_indexers = {
          "tokens": PretrainedTransformerIndexer(model)
      }
  
imports: from typing import List, Iterable, Dict
  import torch
  import pytest
  from allennlp.common.testing import requires_gpu
  from allennlp.data.instance import Instance
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.data.data_loaders import MultiProcessDataLoader, WorkerError
  from allennlp.data.fields import Field, TextField, MetadataField, TensorField
  from allennlp.data.tokenizers import PretrainedTransformerTokenizer
  from allennlp.data.token_indexers import PretrainedTransformerIndexer
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.data.data_loaders.data_collator import LanguageModelingDataCollator
label_desc: Use image node_id 1 to create a new MockDatasetReader object from inherited base classes: DatasetReader with example: obj = MockDatasetReader(model)